DATA ANALYTICS CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ANALYTICS COURSE LEAD MENTORS

DATA ANALYTICS COURSE FEE IN INDIA

Live Virtual

Instructor Led Live Online

110,000
62,423

  • IABAC® & JAINx® Certification
  • 6-Month | 200+ Learning Hours
  • 20 HOURS LEARNING A WEEK
  • 10 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Blended Learning

Self Learning + Live Mentoring

55,000
35,773

  • Self Learning + Live Mentoring
  • IABAC® & JAINx® Certification
  • 1 Year Access To Elearning
  • 10 Capstone & 1 Client Project
  • Job Assistance
  • 24*7 Learner assistance and support

Classroom

In - Person Classroom Training

110,000
67,548

  • IABAC® & JAINx® Certification
  • 6-Month | 200+ Learning Hours
  • 20 HOURS LEARNING A WEEK
  • 10 Capstone & 1 Client Project
  • Cloud Lab Access
  • Internship +Job Assistance

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UPCOMING DATA ANALYTICS ONLINE CLASSES IN INDIA

BEST DATA ANALYTICS CERTIFICATIONS

The entire training includes real-world projects and highly valuable case studies.

IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.

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WHY DATAMITES FOR DATA ANALYTICS TRAINING

Why DataMites Infographic

SYLLABUS OF DATA ANALYTICS CERTIFICATION IN INDIA

MODULE 1: DATA ANALYSIS FOUNDATION

• Data Analysis Introduction
• Data Preparation for Analysis
• Common Data Problems
• Various Tools for Data Analysis
• Evolution of Analytics domain

MODULE 2: CLASSIFICATION OF ANALYTICS

• Four types of the Analytics
• Descriptive Analytics
• Diagnostics Analytics
• Predictive Analytics
• Prescriptive Analytics
• Human Input in Various type of Analytics

MODULE 3: CRIP-DM Model

• Introduction to CRIP-DM Model
• Business Understanding
• Data Understanding
• Data Preparation
• Modeling
• Evaluation
• Deploying
• Monitoring

MODULE 4: UNIVARIATE DATA ANALYSIS

• Summary statistics -Determines the value’s center and spread.
• Measure of Central Tendencies: Mean, Median and Mode
• Measures of Variability: Range, Interquartile range, Variance and Standard Deviation
• Frequency table -This shows how frequently various values occur.
• Charts -A visual representation of the distribution of values.

MODULE 5: DATA ANALYSIS WITH VISUAL CHARTS

• Line Chart
• Column/Bar Chart
• Waterfall Chart
• Tree Map Chart
• Box Plot

MODULE 6: BI-VARIATE DATA ANALYSIS

• Scatter Plots
• Regression Analysis
• Correlation Coefficients

MODULE 1: PYTHON BASICS

• Introduction of python
• Installation of Python and IDE
• Python objects
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
• Operator’s precedence and associativity

MODULE 2: PYTHON CONTROL STATEMENTS

• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements

MODULE 3: PYTHON DATA STRUCTURES

• Basic data structure in python
• String object basics and inbuilt methods
• List: Object, methods, comprehensions
• Tuple: Object, methods, comprehensions
• Sets: Object, methods, comprehensions
• Dictionary: Object, methods, comprehensions

MODULE 4: PYTHON FUNCTIONS

• Functions basics
• Function Parameter passing
• Iterators
• Generator functions
• Lambda functions
• Map, reduce, filter functions

MODULE 5: PYTHON NUMPY PACKAGE

• NumPy Introduction
• Array – Data Structure
• Core Numpy functions
• Matrix Operations

MODULE 6: PYTHON PANDAS PACKAGE

• Pandas functions
• Data Frame and Series – Data Structure
• Data munging with Pandas
• Imputation and outlier analysis

MODULE 1 : OVERVIEW OF STATISTICS 

  • Descriptive And Inferential Statistics
  • Basic Terms Of Statistics
  • Types Of Data

MODULE 2 : HARNESSING DATA 

  • Random Sampling
  • Sampling With Replacement And Without Replacement
  • Cochran's  Minimum Sample Size
  • Simple Random Sampling
  • Stratified Random Sampling
  • Cluster Random Sampling
  • Systematic Random Sampling
  • Biased Random Sampling Methods
  • Sampling Error
  • Methods Of Collecting Data

MODULE 3 : EXPLORATORY DATA ANALYSIS 

  • Exploratory Data Analysis Introduction
  • Measures Of Central Tendencies: Mean, Median And Mode
  • Measures Of Central Tendencies: Range, Variance And Standard Deviation
  • Data Distribution Plot: Histogram
  • Normal Distribution
  • Z Value / Standard Value
  • Empherical Rule  and Outliers
  • Central Limit Theorem
  • Normality Testing
  • Skewness & Kurtosis
  • Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance

MODULE 4 : HYPOTHESIS TESTING 

  • Hypothesis Testing Introduction
  • P- Value, Confidence Interval
  • Parametric Hypothesis Testing Methods
  • Hypothesis Testing Errors : Type I And Type Ii
  • One Sample T-test
  • Two Sample Independent T-test
  • Two Sample Relation T-test
  • One Way Anova Test

MODULE 5 : CORRELATION AND REGRESSION

  • Correlation Introduction
  • Direct/Positive Correlation
  • Indirect/Negative Correlation
  • Regression
  • Choosing Right Method
     

MODULE 1: COMPARISION AND CORRELATION ANALYSIS

• Data comparison Introduction
• Concept of Correlation
• Calculating Correlation with Excel
• Comparison vs Correlation
• Performing Comparison Analysis on Data
• Performing correlation Analysis on Data
• Hands-on case study 1: Comparison Analysis
• Hands-on case study 2 Correlation Analysis

MODULE 2: VARIANCE AND FREQUENCY ANALYSIS

• Concept of Variability and Variance
• Data Preparation for Variance Analysis
• Business use cases for Variance and Frequency Analysis
• Performing Variance and Frequency Analysis
• Hands-on case study 1: Variance Analysis
• Hands-on case study 2: Frequency Analysis

MODULE 3: RANKING ANALYSIS

• Introduction to Ranking Analysis
• Data Preparation for Ranking Analysis
• Performing Ranking Analysis with Excel
• Insights for Ranking Analysis
• Hands-on Case Study: Ranking Analysis

MODULE 4: BREAK EVEN ANALYSIS

• Concept of Breakeven Analysis
• Make or Buy Decision with Break Even
• Preparing Data for Breakeven Analysis
• Hands-on Case Study: Procurement Decision with break even

MODULE 5: PARETO (80/20 RULE) ANALSYSIS

• Pareto rule Introduction
• Preparation Data for Pareto Analysis
• Insights on Optimizing Operations with Pareto Analysis
• Performing Pareto Analysis on Data
• Hands-on case study: Pareto Analysis

MODULE 6: Time Series and Trend Analysis

• Introduction to Time Series Data
• Preparing data for Time Series Analysis
• Types of Trends
• Trend Analysis of the Data with Excel
• Insights from Trend Analysis
• Hands-on Case Study: Trend Analysis

MODULE 7: DATA ANALYSIS BUSINESS REPORTING

• Management Information System Introduction
• Various Data Reporting formats
• Creating Data Analysis reports as per the requirements
• Presenting the reports
• Hands-on case study: Create Data Analysis Reports

MODULE 1: DATA ANALYTICS FOUNDATION

• Business Analytics Overview
• Application of Business Analytics
• Visual Perspective
• Benefits of Business Analytics
• Challenges
• Classification of Business Analytics
• Data Sources
• Data Reliability and Validity
• Business Analytics Model

MODULE 2: OPTIMIZATION MODELS

• Prescriptive Analytics with Low Uncertainty
• Mathematical Modeling and Decision Modeling
• Break Even Analysis
• Product Pricing with Prescriptive Modeling
• Building an Optimization Model
• Case Study 1 : WonderZon Network Optimization
• Assignment 1 : KERC Inc, Optimum Manufacturing Quantity

MODULE 3: PREDICTIVE ANALYTICS WITH REGRESSION

• Mathematics beyond Linear Regression
• Hands on: Regression Modeling in Excel
• Case Study 2 : Sales Promotion Decision with Regression Analysis
• Assignment 2 : Design Marketing Decision board for QuikMark Inc.

MODULE 4: DECISION MODELING

• Prescriptive Analytics with High Uncertainty
• Comparing Decisions in Uncertain Settings
• Decision Trees for Decision Modeling
• Case Study 3 : Decision modeling of Internet Plans, Monte Carlo Simulation
• Case Study 4 : Kickathlon Sports Retailer Supplier Decision Modeling

MODULE 1: MACHINE LEARNING INTRODUCTION

• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification And Regression
• Supervised Vs Unsupervised

MODULE 2: ML ALGO: LINEAR REGRESSSION

• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Hands-on Linear Regression with ML Tool

MODULE 3: ML ALGO: LOGISTIC REGRESSION

• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Hands-on Logistics Regression with ML Tool

MODULE 4: ML ALGO: KNN

• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Hands-on KNN with ML Tool

MODULE 5: ML ALGO: K MEANS CLUSTERING

• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works : K Means theory
• Hands-on K Means Clustering with ML Tool

MODULE 6: ML ALGO: DECISION TREE

• Random Forest Ensemble technique
• How it works: Bagging Theory
• Hands-on Decision Tree with ML Tool

MODULE 7: ML ALGO: SUPPORT VECTOR MACHINE (SVM)

• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Modeling and Evaluation of SVM in Python

MODULE 8: ARTIFICIAL NEURAL NETWORK (ANN)

• Introduction to ANN
• How It Works: Back prop, Gradient Descent
• Modeling and Evaluation of ANN in Python

MODULE 9: PROJECT: PREDICTIVE ANALYTICS WITH ML

• Project Business requirements
• Data Modeling
• Building Predictive Model with ML Tool
• Evaluation and Deployment
• Project Documentation and Report

MODULE 1: GIT INTRODUCTION

• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture

MODULE 2: GIT REPOSITORY and GitHub

• Git Repo Introduction
• Create New Repo with Init command
• Copying existing repo
• Git user and remote node
• Git Status and rebase
• Review Repo History
• GitHub Cloud Remote Repo

MODULE 3: COMMITS, PULL, FETCH AND PUSH

• Code commits
• Pull, Fetch and conflicts resolution
• Pushing to Remote Repo

MODULE 4: TAGGING, BRANCHING AND MERGING

• Organize code with branches
• Checkout branch
• Merge branches

MODULE 5: UNDOING CHANGES

• Editing Commits
• Commit command Amend flag
• Git reset and revert

MODULE 6: GIT WITH GITHUB AND BITBUCKET

• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers
• Bitbucket Git account

MODULE 1: DATABASE INTRODUCTION

• DATABASE Overview
• Key concepts of database management
• CRUD Operations
• Relational Database Management System
• RDBMS vs No-SQL (Document DB)

MODULE 2: SQL BASICS

• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation
• Comments
• import and export dataset

MODULE 3: DATA TYPES AND CONSTRAINTS

• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment

MODULE 4: DATABASES AND TABLES (MySQL)

• Create database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create new table from existing data types
• Insert into, Update records
• Alter table

MODULE 5: SQL JOINS

• Inner join
• Outer join
• Left join
• Right join
• Cross join
• Self join

MODULE 6: SQL COMMANDS AND CLAUSES

• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries

MODULE 7: DOCUMENT DB/NO-SQL DB

• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods
• MongoDB data management

MODULE 1: BIG DATA INTRODUCTION

• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• Big Data Analytics Introduction

MODULE 2: HDFS AND MAP REDUCE

• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Mapping and reducing stages concepts
• Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort
• Hands-on Map Reduce task

MODULE 3: PYSPARK FOUNDATION

• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs

MODULE 4: SPARK SQL and HADOOP HIVE

• Introducing Spark SQL
• Spark SQL vs Hadoop Hive
• Working with Spark SQL Query Language

MODULE 5: MACHINE LEARNING WITH SPARK ML

• Introduction to MLlib Various ML algorithms supported by Mlib
• ML model with Spark ML.
• Linear regression
• logistic regression
• Random forest

MODULE 6: KAFKA and Spark

• Kafka architecture
• Kafka workflow
• Configuring Kafka cluster
• Operations

MODULE 1: BUSINESS INTELLIGENCE INTRODUCTION

• What Is Business Intelligence (BI)?
• What Bi Is The Core Of Business Decisions?
• BI Evolution
• Business Intelligence Vs Business Analytics
• Data Driven Decisions With Bi Tools
• The Crisp-Dm Methodology

MODULE 2: BI WITH TABLEAU: INTRODUCTION

• The Tableau Interface
• Tableau Workbook, Sheets And Dashboards
• Filter Shelf, Rows And Columns
• Dimensions And Measures
• Distributing And Publishing

MODULE 3: TABLEAU: CONNECTING TO DATA SOURCE

• Connecting To Data File , Database Servers
• Managing Fields
• Managing Extracts
• Saving And Publishing Data Sources
• Data Prep With Text And Excel Files
• Join Types With Union
• Cross-Database Joins
• Data Blending
• Connecting To Pdfs

MODULE 4: TABLEAU : BUSINESS INSIGHTS

• Getting Started With Visual Analytics
• Drill Down And Hierarchies
• Sorting & Grouping
• Creating And Working Sets
• Using The Filter Shelf
• Interactive Filters
• Parameters
• The Formatting Pane
• Trend Lines & Reference Lines
• Forecasting
• Clustering

MODULE 5: DASHBOARDS, STORIES AND PAGES

• Dashboards And Stories Introduction
• Building A Dashboard
• Dashboard Objects
• Dashboard Formatting
• Dashboard Interactivity Using Actions
• Story Points
• Animation With Pages

MODULE 6: BI WITH POWER-BI

• Power BI basics
• Basics Visualizations
• Business Insights with Power BI

OFFERED DATA ANALYTICS COURSES IN INDIA

DATA ANALYTICS TRAINING COURSE REVIEWS

ABOUT DATA ANALYTICS COURSE IN INDIA

The landscape of data analytics is undergoing a transformation that's nothing short of revolutionary. The Big Data Analytics market in India is currently valued at $2 Billion and is expected to grow at a CAGR of 26 percent reaching approximately $16 Billion by 2025, making India's share approximately 32 percent in the overall global market. (Silicon India)  In essence, data analytics is not merely a trend but a transformative force reshaping industries on a worldwide scale.

DataMites Institute presents an intensive Data Analytics certification program available across India, designed to prepare individuals for a thriving career in this dynamic field. The 4-month Certified Data Analyst Training in India offers over 200 hours of deep-dive learning experiences. It covers essential topics such as statistical analysis, data visualization, machine learning, and predictive analytics. Learners can expect to commit around 20 hours weekly, gaining a thorough mastery of data analytics. The practical aspect of the course is emphasized through 10 Capstone Projects and a Client Project, allowing students to translate theoretical knowledge into actionable insights.

Datamites provides offline data analytics courses across multiple cities, including Bangalore, Bhubaneswar, Chennai, Nagpur, Kochi, Ahmedabad, Pune, Mumbai, Delhi, Hyderabad, Kolkata and Vijayawada.

Here are ten reasons why DataMites is the go-to institute for Data Analytics Courses in India:

  1. The faculty at DataMites includes seasoned experts like Ashok Veda, whose practical wisdom enriches the learning experience.

  2. A comprehensive curriculum awaits students, touching on pivotal subjects from statistical analysis to machine learning, paving the way for a career in data analysis.

  3. Graduates of the program receive internationally recognized certifications from bodies such as IABAC, NASSCOM FutureSkills Prime, and JainX, enhancing their career profiles significantly.

  4. DataMites caters to varying learning preferences, offering both data analytics offline courses in India and flexible online data analytics training in India.

  5. The program's emphasis on practical application is evident with project-based learning involving real data, preparing students for real-world analytical challenges.

  6. Data Analytics Internship in India available through DataMites offer a glimpse into the industry, granting students valuable exposure.

  7. Job placement support is a pillar of the DataMites experience, with career services and referrals to help students navigate the job market successfully.

  8. Supplemental learning materials, including hardcopy resources and books, deepen the educational journey.

  9. Joining DataMites means becoming part of an exclusive learning community, a network of data enthusiasts and professionals.

  10. Accessible training is a priority, with affordable pricing and scholarship opportunities for qualified students.

India presents an ideal landscape for aspiring data analysts, combining a dynamic economic climate with a wealth of intellectual resources and cultural diversity. This environment offers a nurturing backdrop for those embarking on a career in data analytics. DataMites' Data Analytics Certification in India equips individuals with essential analytical skills, placing them at the heart of an energetic and supportive professional ecosystem. 

The country's economic vigor, alongside its rich intellectual and cultural heritage, provides a fertile ground for professional growth in data analytics. This is reflected in the substantial number of opportunities available – as evidenced by over 87,000+ Data Analyst job openings in India on LinkedIn. This vibrant setting ensures that those who pursue Data Analytics certification with DataMites are well-positioned to thrive in this rapidly evolving field.

ABOUT DATAMITES DATA ANALYTICS TRAINING IN INDIA

Data Analytics is the systematic approach to examining large datasets to uncover patterns, derive insights, and interpret information for informed decision-making in business. It involves various statistical techniques and tools to analyze and transform data into meaningful information.

Industries such as finance, healthcare, retail, e-commerce, marketing, telecommunications, and manufacturing employ Data Analytics extensively. They use it to understand customer behavior, optimize operations, predict trends, and enhance decision-making processes.

Data Analytics has a broad and continually expanding scope. The field is gaining importance with the surge of big data and technological advancements. It's becoming essential for companies to analyze and interpret vast amounts of data to stay competitive, leading to a higher demand for data analytics professionals.

Career prospects in Data Analytics are diverse and promising. Positions like Data Analyst, Data Scientist, Business Analyst, Data Engineer, and Data Architect are in high demand. These roles span various sectors and offer the potential for professional growth, specialization in specific analytics areas, and opportunities to transition into leadership positions.

A Data Analyst's Salary in India is generally around INR ₹6,00,000 per annum. This can vary based on the analyst's experience, the complexity of the role, and the specific industry. (Glassdoor)

DataMites is highly recommended for those looking to pursue Data Analytics Training in India. It is recognized for its robust curriculum that aligns with industry standards and provides practical, hands-on experience.

The "Certified Data Analyst" course offered by DataMites is considered one of the best for aspirants. It covers extensive data analysis techniques, statistical analysis, and includes exposure to machine learning, preparing students for a variety of roles in the analytics domain.

The Data Analytics course fee in India ranges from ₹40,000 to ₹80,000. The fee varies depending on the course duration, the depth of the material covered, and the reputation of the institute.

While not strictly mandatory, coding skills greatly enhance a Data Analyst's toolkit. Proficiency in languages like Python, R, and SQL, as well as in tools like Excel and Tableau, can greatly expand the range of tasks a Data Analyst can perform and improve their efficiency.

The typical starting salary for a Data Analyst Fresher in India is approximately INR ₹402,820 per annum, which translates to about ₹1.6 Lakhs per year. (Payscale)

Being a Data Analyst can be challenging due to the need for a blend of analytical thinking, problem-solving, and technical skills. The role often requires a keen eye for detail, the ability to work with complex datasets, and staying updated with the latest analytics trends and tools.

Data Analytics is a compelling career choice for fresh graduates, as it offers robust growth prospects, the opportunity to work in various industries, and competitive salaries. The demand for data-driven insights makes it a field with a lot of potential for new entrants.

While a degree can provide a strong foundation, it is not always necessary to pursue a career in Data Analytics. Many employers value practical experience, specialized certifications, and a demonstrated ability to generate insights from data.

Securing a position as a Data Analyst without prior experience can be challenging but feasible. Gaining relevant qualifications, building a portfolio of data projects, and acquiring certifications can enhance a candidate’s employability, even without traditional experience.

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FAQ'S OF DATA ANALYTICS TRAINING IN INDIA

DataMites is a preferred choice for Data Analytics training in India due to its comprehensive curriculum, practical training approach, and the expertise of its trainers. The institute offers a blend of theoretical knowledge and practical experience, ensuring that students are job-ready upon completion.

DataMites' Certified Data Analyst Course in India stands out due to its curriculum that is closely aligned with industry needs, the practical experience it offers, and the global certifications that add value to a learner's professional profile. The course is designed to equip students with both the knowledge and the practical skills required in the field.

Before enrolling in a data analytics course, it's advantageous to have a fundamental grasp of math and statistics, along with basic computer literacy. These skills will help in understanding the course content more effectively.

The Certified Data Analyst Course by DataMites is open to anyone interested in data analysis, including graduates, professionals looking to enhance their skills, and any individual motivated to learn about data analytics.

The Data Analytics Course Training Fee in India by DataMites typically ranges from INR 28,178 to INR 76,000, varying with the course's duration, delivery mode, and any additional features included.

The duration of the Certified Data Analytics Course by DataMites in India extends over 4 months, comprising more than 200 hours of comprehensive training, inclusive of practical projects and exercises.

The Certified Data Analyst Training at DataMites encompasses subjects such as data analysis methodologies, statistical analysis, data visualization, and machine learning, amongst others.

DataMites' Flexi-Pass is a program offering access to various courses at a reduced rate, providing learners the flexibility to select and attend different courses based on their learning objectives and schedules.

DataMites accepts a range of payment methods, including online payment systems and bank transfers. It's advisable to contact them directly for the most up-to-date payment options.

Upon successful completion of a Data Analytics course at DataMites, participants receive globally recognized certifications from bodies like IABAC, NASSCOM FutureSkills Prime, and JainX, validating their learned skills in data analytics.

The trainers leading the Data Analytics Courses at DataMites are seasoned professionals with extensive experience in the data analytics industry.

DataMites offers a variety of training modes for Data Analytics, including in-person classroom sessions, live online classes, corporate training for businesses, and flexible self-paced study options.

DataMites often provides trial classes or demonstrations to allow prospective students to get a feel for the course structure and teaching style before committing to payment.

Yes, DataMites provides in-person, interactive for data analytics classroom training in India, subject to demand. These sessions are led by expert instructors and are designed to facilitate an engaging learning experience.

The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -

  • 1. Job connect
  • 2. Resume Building
  • 3. Mock interview with industry experts
  • 4. Interview questions

The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.

No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.

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